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Record W3207131610 · doi:10.12678/1089-313x.061522b

Injury Rates and Characteristics Associated with Participation in Organized Dance Education: A Systematic Review

2022· review· en· W3207131610 on OpenAlexaff
M. Critchley, Sarah Kenny, Ashleigh Ritchie, Carly McKay

Bibliographic record

VenueJournal of Dance Medicine & Science · 2022
Typereview
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsDanceCINAHLInclusion (mineral)Dance educationPsychologyMedicinePhysical therapyPsychological interventionNursingVisual artsSocial psychologyArt

Abstract

fetched live from OpenAlex

INTRODUCTION: Several studies and recent systematic reviews have investigated injury in dance settings and have largely focused on specific concert dance genres (i.e., ballet, contemporary) and elite levels (i.e., pre-professional, professional) of dance. Less is known about the health of those who participate in dance education settings, namely teachers and students from private dance studios. Given that these individuals constitute a large proportion of the dance community, greater clarity of risks in the dance training environment could benefit an underserved majority by informing the development of effective injury prevention strategies. Objective: The primary objective was to describe injury rates and characteristics associated with participation in organized dance education settings. Methods: Six electronic databases were searched to April 2021 (Medline, EMBASE, SportDiscus, CINAHL, SCOPUS, Cochrane). Selected studies met a priori inclusion criteria that required original data from dance teacher and student samples within formal dance education settings. All genres of dance were eligible. Studies were excluded if no injury outcomes or estimates of dance exposure were reported, if injuries occurred during rehearsal and performance, or if dance was used as a therapeutic intervention or exercise. Two reviewers independently assessed each paper for inclusion at abstract and full text screening stages. The quality of included studies was assessed using the Joanna Briggs Institute Level of Evidence tool. Results: The initial database search identified 1,424 potentially relevant records, 26 were included and scored. Most studies (n = 22) focused on dance students only, three included only dance teachers, and one study included both. Among both dance students and teachers, the majority of injuries reported were overuse or chronic and involved the lower limb. For studies that reported injury rates (n = 14), estimates ranged from 0.8 to 4.7 injuries per 1,000 dance hours, 4.86 per 1,000 dancer-days, and 0.21 to 0.34 per 1,000 dance exposures. Conclusions: Based on the current research, dance students and teachers experience a similar rate of injury to concert and professional dancers, and their injuries are most commonly overuse injuries involving the lower extremity. There have been few high-quality investigations of injury specific to the dance training environment. Therefore, consensus around the burden of injury in the dance education settings remains difficult. Future dance epidemiological investigations that examine the burden of injury among dance teachers and students, include operational injury and exposure definitions, and utilize prospective designs are warranted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.068
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0110.010
Bibliometrics0.0200.025
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.423
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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